Inderjit Dhillon

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Joel Tropp

Websitehttps://www.cs.utexas.edu/~inderjit/

Inderjit Singh Dhillon (/ˈthɪloʊn/) is the Gottesman Family Centennial Professor of Computer Science and Mathematics and Director of the ICES Center for Big Data Analytics at the University of Texas at Austin. His main research interests are in machine learning, computational learning theory, mathematical optimisation, linear algebra, data analysis, parallel computing and network analysis.

Biography

Dhillon received his B.Tech. degree from the Indian Institute of Technology, Bombay in 1989. He subsequently worked at AT&T Bell Laboratories as a Research Staff Member under Dr. Narendra Karmarkar. He received his Ph.D. from the University of California at Berkeley in 1997 under the direction of Beresford Parlett and James Demmel. Dhillon joined the Computer Science faculty at the University of Texas at Austin in 1999.^{[citation needed]}

Academic works

Dhillon's main research interests are in machine learning, data analysis and computational mathematics. His emphasis is on developing novel algorithms that respect the underlying problem structure and are scalable to large data sets.^[7]

Honors and awards

Dhillon is a fellow of the Association for Computing Machinery (ACM),^[2] a fellow of the Institute of Electrical and Electronics Engineers (IEEE),^[3] a fellow of the Society for Industrial and Applied Mathematics (SIAM),^[4] and a fellow of the American Association for the Advancement of Science (AAAS).^[1]

References

^ ^a ^b "2016 AAAS Fellows approved by the AAAS Council". Science. American Academy of Arts and Sciences. 25 November 2016. doi:10.1126/science.354.6315.981.
^ ^a ^b "ACM Fellow (2014): Inderjit Dhillon". Association for Computing Machinery. 2014. For contributions to large-scale data analysis, machine learning and computational mathematics.
^ ^a ^b "2014 IEEE Fellows Announced". Institute of Electronic and Electrical Engineers. 12 December 2013.
^ ^a ^b "Class of 2014". Society for Industrial and Applied Mathematics.
^ "SIAM Activity Group on Linear Algebra: Prizes". Society for Industrial and Applied Mathematics.
^ "SIAM Outstanding Paper Prizes". Society for Industrial and Applied Mathematics.
^ "Nomadic computing speeds up Big Data analytics". National Science Foundation. 4 November 2015. He is among those who have realized it's possible to tame highly complex data (or "data with high dimensionality," in the lingo of the field) by using machine learning to reduce data to its most meaningful parameters. His approaches are widely adopted in science and industry.